Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Anchor drag alarm from a GPS track
AClaude Opus 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener
- Estimated cost
- $0.18
- Wall time
- 35s
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Anchor drag alarm from a GPS track `input.json` (synthetic, not for real anchor watch): `anchor` [lat, lon], `rode_m`, `depth_m`, `bow_height_m`, `gps_offset_m`, `margin_m`, `consecutive` N, and `track` (CSV `t,lat,lon`, t in seconds). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `anchor.svg`. Fresh inputs are used too. ## Rules 1. Alarm radius = sqrt(rode^2 - (depth + bow_height)^2) (0 if rode <= depth + bow_height) + gps_offset + margin. 2. Track rows: skip the header. A row is **bad** (counted, skipped) if it has fewer than 3 fields, t is not an integer, lat/lon is not a finite number in range [-90, 90] / [-180, 180], or t is not greater than the last *valid* row's t. 3. Distance from the anchor (metres): R = 6371000; m = mean of the anchor and point latitudes; x = radians(dlon) * cos(m) * R (east), y = radians(dlat) * R (north); distance = hypot(x, y). A point is **outside** if distance > radius. 4. An **event** is a maximal run of consecutive valid points that are outside, with length >= N. Report `start_t` (first point), `trigger_t` (Nth point), `end_t` (last point), `max_dist_m`, and `bearing_deg` = atan2(x, y) mod 360 from the anchor to the run's last point. 5. Round the radius to 2 decimals and distances and bearings to 1. ## Visual output: anchor.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with `<circle id="swing-circle">`, `<polyline id="track">` and an element `id="anchor"`. The drawing itself is not graded. ## answer.json `{"radius_m", "valid_points", "bad_rows", "events": [...], "max_dist_m", "outside_points"}` ## Scoring Hidden checks (partial credit; all must pass): `swing_radius` (within 0.011), `track_cleaning` (exact), `distances`, `alarm_events` (times exact, values within 0.11), `anchor_svg_static` (anchor.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. 60-second limit per run.
Catenary-free swing radius with offsets, rejecting bad and out-of-order rows against the last valid time, short excursions that must not trigger, and a real drag run with trigger time and bearing. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked).
Benchmark code for comparing AI models, not navigation or safety software.
Success criteria: 1. swing_radius 2. track_cleaning 3. distances 4. alarm_events 5. anchor_svg_static 6. fresh_run_1 7. fresh_run_2
Setup notes: Use Case Scout batch 10 case k07-anchor-drag-alarm (offline fixture). Prepared with batch10/prepare.sh; hidden key/tests kept outside the agent's work dir. Prompt = TASK.md verbatim. Scored with the scout's scorer: `python score.py WORKDIR KEYDIR`. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. Visual output (anchor.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. Claude Code's own reported cost for claude-opus-5.5: $0.184151. Score: 7/7. Cost is an ESTIMATE: measured tokens x pricing.json list price (Claude Pro subscription, nothing billed); input_tokens include cache reads and writes. Single run.
Estimated cost List-price estimate; not actually billed.
Output
Metrics
- Estimated cost
- $0.18
- Wall time
- 35s
- Human step-ins
- 0
- Turns
- 5
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on | 100,848 | 3,773 | $0.18 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Claude Opus 5.5 (anthropic/claude-opus-5.5, effort default (no --effort flag) · thinking on) via
Claude Code 2.1.299 (headless -p), cloud. - It counts as a success only if it meets every success criterion: 1. swing_radius 2. track_cleaning 3. distances 4. alarm_events 5. anchor_svg_static 6. fresh_run_1 7. fresh_run_2
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
Was this useful?
Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
[
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"output_tokens": 3773,
"output_tokens_details": {
"thinking_tokens": 545
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"server_tool_use": {
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"service_tier": "standard",
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"type": "message"
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"webSearchRequests": 0,
"costUSD": 0.1841508,
"contextWindow": 1000000,
"maxOutputTokens": 128000,
"thinkingTokens": 545,
"canonicalModel": "claude-opus-5-5",
"provider": "firstParty",
"costBasis": "list"
}
},
"permission_denials": [],
"terminal_reason": "completed",
"fast_mode_state": "off",
"fast_mode_disabled_reason": "sdk_opt_in_required",
"subagent_stats": {
"spawned": 0,
"requested": {
"background": 0,
"foreground": 0,
"unset": 0
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"started_in_background": 0,
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"refused": {
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"safety_stops": 0,
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"num_turns": 5,
"subtype": "success",
"api_error_status": null,
"result": "`solve.py` is written and runs on the provided input. `python solve.py input.json answer.json` writes `answer.json` and `anchor.svg` using only the standard library. I haven't checked these numbers against an independent calculation.\n\n**Result on `input.json`:**\n- **Alarm radius:** 38.15 m.\n- **Track points:** 290 valid, 10 bad. Nine of the bad rows have a missing or `NaNx` lat/lon. The tenth is most likely a timestamp that doesn't increase.\n- **Points outside the radius:** 49. The farthest is 50.5 m from the anchor.\n- **Three alarm events:**\n\n| start_t | trigger_t | end_t | max_dist_m | bearing_deg |\n|---|---|---|---|---|\n| 822 | 852 | 902 | 42.9 | 68.2 |\n| 2640 | 2660 | 2967 | 50.5 | 314.9 |\n| 2988 | 3009 | 3071 | 43.6 | 301.8 |\n\n**Choices I made where the rules weren't explicit:**\n- **`track` location:** read as a file path relative to `input.json`. If it contains newlines, it's treated as the CSV text itself.\n- **Integer t:** `10` is accepted, but `10.0` and `1e3` count as bad rows.\n- **Empty lines:** skipped without being counted as bad. If the grader counts them, `bad_rows` would be off on inputs that have blank lines.\n- **Where `anchor.svg` goes:** written to the current directory, and also next to `answer.json` if that's a different folder.\n\n`anchor.svg` is a static drawing with no scripts, images or links. It has the required `swing-circle`, `track` and `anchor` ids, and points outside the radius are marked in red. It overwrote the `anchor.svg` that was already in the folder.",
"ttft_ms": 2250,
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"uuid": "[REDACTED]",
"ttft_stream_ms": 1086,
"time_to_request_ms": 241,
"first_content_frame_ms": 1086,
"queued_turn_count": 0,
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]